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10th Composites and Advanced Materials Expo, CAMX 2024

作者： Pundhir, N. Heskin, M. Luo, W. Chandrashekhara, K. Wilcox, L. Donnell, K. Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology RollaMO65409 United States Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

ISBN: (纸本)9781934551462

The unique combination of properties offered by carbon fiber-reinforced polymer composites has led to their widespread adoption in aerospace, automotive, and other industries due to their high specific stiffness and strength properties. The present study investigates the potential of vacuum-assisted microwave curing for carbon fiber-reinforced polymer composites which presents several advantages over traditional autoclave curing methods. Microwave curing offers reduced cycle times, volumetric heating, enhanced safety, and lower energy consumption. This comprehensive approach evaluates the feasibility and efficacy of vacuum-assisted microwave curing for carbon fiber-reinforced polymer composites by comparing the manufactured composites against traditional autoclave composites. Mechanical properties and microstructural characteristics were measured and examined to accomplish this. In the present work, industrially available IM7/Cycom 5320-1 unidirectional prepreg with 33% resin by weight and a fiber area weight of 145 g/m2 is employed to manufacture the laminated composite. A sixteen-layer symmetric cross-ply ([00/900]4s) and quasi-isotropic ([450/900/-450/00]2s) laminated composites are manufactured using a custom-built microwave curing chamber. Mechanical characterization of the microwave-cured composite is conducted through uniaxial tensile and flexural tests, with subsequent comparison to autoclave-cured samples. Optical microscopy is employed to estimate the quality of the manufactured samples. The results obtained from this study support advancements in composite manufacturing processes, particularly in the aerospace and automotive sectors. Copyright 2024. Used by CAMX – The Composites and Advanced Materials Expo.

关键词： Laminated composites

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Parallel Binary QUasi-Affine TRansformation Evolution (QUATRE) Algorithm 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Zhuang, Zhongjie Chu, Shu-Chuan Pan, Jeng-Shyang Snašel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Science and Engineering Flinders University 1284 South Road Clovelly ParkSA5042 Australia Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

QUasi-Affine TRansformation Evolutionary (QUATRE) generalized differential evolution algorithm to matrix form, which was designed for solving continuous problems. Binary QUATRE (BQUATRE) algorithm convert the original BQUATRE to the binary form to deal with hyperspectral dimensionality reduction. In this paper, in order to further improve the performance of the BQUATRE, the parallel mechanism is introduced to BQUATRE, and proposed parallel binary BQUATRE algorithm (PBQUATRE). The global best solution and the group best solution both guide the position update of other solutions with a probability of 0.5 in communication phase. The mutation matrix B is also affected not only by the global best solution but also by the group best solution. The proposed algorithm is applied for feature selection and hyperspectral dimensionality reduction, and experimental results show that the proposed PBQUATRE is superior to the classics and state-of-the-art algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linear transformations

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhou, Jieming Zhang, Tong Fang, Pengfei Petersson, Lars Harandi, Mehrtash Australian National University College of Engineering and Computer Science Canberra Australia Image and Visual Representation Laboratory Epfl Lausanne Switzerland Imaging and Computer Vision Group Csiro Canberra Australia Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350382204

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs' designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model's performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks. © 2023 IEEE.

关键词： Graph neural networks

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Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation 31

Extending LLMs to New Languages: A Case Study of Llama and P...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761964

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model's performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model's initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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COMPASS: Compiler Pass Selection For Improving Fidelity Of NISQ Applications

COMPASS: Compiler Pass Selection For Improving Fidelity Of N...

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IEEE International Conference on Rebooting Computing (ICRC)

作者： Siddharth Dangwal Gokul Subramanian Ravi Lennart Maximilian Seifert Poulami Das James Sud Frederic T. Chong Department of Computer Science University of Chicago Department of Computer Science and Engineering University of Michigan Department of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (数字)9798331541279

ISBN: (纸本)9798331541286

Noisy qubit devices limit the fidelity of programs executed on near-term or Noisy Intermediate Scale Quantum (NISQ) systems. The fidelity of NISQ applications can be improved by using various optimizations during program compilation (or transpilation). These optimizations or passes are designed to minimize circuit depth (or program duration), steer more computations on devices with lowest error rates, and reduce the communication overheads involved in performing two-qubit operations between non-adjacent qubits. Additionally, standalone optimizations have been proposed to reduce the impact of crosstalk, measurement, idling, and correlated errors. However, our experiments using real IBM quantum hardware show that using all optimizations simultaneously often leads to sub-optimal performance and the highest improvement in application fidelity is obtained when only a subset of passes are used. Unfortunately, identifying the optimal pass combination is non-trivial as it depends on the application and device specific *** this paper, we propose COMPASS, an automated software framework for optimal Compiler Pass Selection for quantum programs. COMPASS uses dummy circuits that resemble a given program but is composed of only Clifford gates and thus, can be efficiently simulated classically to obtain its correct output. The optimal pass set for the dummy circuit is identified by evaluating the efficacy of different pass combinations and this set is then used to compile the given program. Our experiments using real IBMQ machines show that COMPASS improves the application fidelity by 4.3x on average and by up-to 248.8x compared to the baseline. However, the complexity of this search scales exponential in the number of compiler steps. To overcome this drawback, we propose Efficient COMPASS (E-COMPASS) that leverages a divide-and-conquer approach to split the passes into sub-groups and exhaustively searching within each sub-group. Our evaluations show that E-COMPASS impro

关键词： Performance evaluation Program processors Translation Qubit Measurement uncertainty Software Noise measurement Object recognition Compass Optimization

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Heterogeneous Memory Allocation Scheme for Batch Jobs with Intel Optane DC Persistent Memory and DRAM 9

Heterogeneous Memory Allocation Scheme for Batch Jobs with I...

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9th International Conference on Applied System Innovation, ICASI 2023

作者： Chang, Che-Wei Zhang, Kai-Jie Chang Gung University School of Electrical and Computer Engineering College of Engineering Department of Computer Science and Information Engineering No.259 Wenhua 1st Rd. Guishan Dist. Taoyuan333 Taiwan

ISBN: (纸本)9798350398380

It is a promising approach to include Non-Volatile Memory (NVM) and DRAM together as the main memory to provide huge memory space for large objects and guarantee short access latency for frequent writes and reads. To provide feasible and suitable memory allocation for all applications on a sever, our solution analyzes applications to derive the read-write ratio, memory footprint, and access pattern of each application. Heterogenous memory allocation scheme is then developed to reduce the makespan of batch jobs. © 2023 IEEE.

关键词： Dynamic random access storage

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A Global Best-guided Firefly Algorithm for engineering Problems

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Journal of Bionic engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization engineering design

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Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation

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computers in Biology and Medicine 2024年 173卷 108345-108345页

作者： Hosseini Chagahi, Mehdi Mohammadi Dashtaki, Saeed Moshiri, Behzad Jalil Piran, M.D. School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of

Due to their widespread prevalence and impact on quality of life, cardiovascular diseases (CVD) pose a considerable global health burden. Early detection and intervention can reduce the incidence, severity, and progression of CVD and prevent premature death. The application of machine learning (ML) techniques to early CVD detection is therefore a valuable approach. In this paper, A stack-based ensemble classifier with an aggregation layer and the dependent ordered weighted averaging (DOWA) operator is proposed for detecting cardiovascular diseases. We propose transforming features using the Johnson transformation technique and normalizing feature distributions. Three diverse first-level classifiers are selected based on their accuracy, and predictions are combined using the aggregation layer and DOWA. A linear support vector machine (SVM) meta-classifier makes the final classification. Adding the aggregation layer to the stacking classifier improves classification accuracy significantly, according to the study. The accuracy is enhanced by 5%, resulting in an impressive overall accuracy of 94.05%. Moreover, the proposed system significantly increases the area under the receiver operating characteristic (ROC) curve compared to recent studies, reaching 97.14%. It further reinforces the classifier's reliability and effectiveness in classifying cardiovascular disease by distinguishing between positive and negative instances. With improved accuracy and a high area under the curve (AUC), the proposed classifier exhibits robustness and superior performance in the detection of cardiovascular diseases. © 2024 Elsevier Ltd

关键词： Support vector machines

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Hybrid Dragonfly and Pattern Search Algorithm Applied to Dynamic Economic Dispatch Problem

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Recent Advances in computer science and Communications 2022年第3期15卷 334-337页

作者： Sahoo, Arun Kumar Panigrahi, Tapas Kumar Nayak, Gopal Krishna Behera, Aurobinda Department of Electrical Engineering IIIT Bhubaneswar Odisha Bhubaneswar751003 India Department of Electrical Engineering PMEC Berhampur Odisha Berhampur India Department of Computer Science & Engineering IIIT Bhubaneswar Odisha Bhubaneswar India

Background: In this paper, a novel hybridisation of the dragonfly algorithm (DFA) with the pattern search (PS) algorithm is applied to the dynamic economic dispatch (DED) problem. The DED problem is non-convex, non-linear, and non-smooth and considering practical constraints such as the loading effect of the valve point and ramp rate limits. The conventional DFA is stuck in the local optima and converges prematurely. Introduction: The characteristics of the optimality of the electric power system are dependent on reliability and high economy. Economic dispatch (ED) significantly contributes in deriving the optimal solutions for the operation of a power system. ED aims to generate electric power at the optimum cost among all generating units in order to satisfy the load demand considering all practical and operational constraints. Practical constraints, such as the effect of steam valves, the dynamic behaviour of ramp rate limits, losses due to transmission of power, and prohibited operating zones, convert the linear and convex problem to a non-convex and non-linear problem. Methods: Planning and scheduling of output electric power from committed generating units to ful-fill the load demand for a scheduled period are termed as DED. The practical generators of thermal power experience the effect of steam valve, real-time ramp rate limit, and technical constraints. DED satisfies all practical, technical, and operational constraints. The DFA was a newly proposed method taking the inspiration of the behaviour of dragonflies for hunting and migrating towards food. The random movement of dragonfly clusters depicts their static characteristics for exploring in the local search space for exploitation competencies, whereas the dynamic behaviour of swarms is used for exploring the global search space by moving in a single direction for a long distance. Result: The efficiency of the proposed method is validated for six well-known benchmark functions. The hybrid technique is com

关键词： Efficiency

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Complex-frequency waves:beat loss and win sensitivity

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Light(science & Applications) 2024年第2期13卷 181-183页

作者： Qingqing Cheng Tao L School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Department of Respiratory Diseases and Critical Medicine Quzhou Hospital Affiliated to Wenzhou Medical UniversityQuzhou324000China National Laboratory of Solid State Microstructures College of Engineering and Applied SciencesNanjing UniversityNanjing210093China

Recent experiments have demonstrated that synthesized complex-frequency waves can impart a virtual gain to molecule sensing systems,which can effectively restore information lost due to intrinsic molecular *** enhancement notably amplifies the signal of trace molecular vibrational fingerprints,thereby substantially improving the upper limit of sensitivity.

关键词： thereby Complex limit

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